Article
Automation & Control Systems
Huiming Duan, Xilin Luo
Summary: This research establishes a novel multivariable Verhulst grey model for predicting coal consumption. By considering factors like population size and area economic development, the model provides more accurate predictions and is applicable to real situations. Experimental results show that this model outperforms other commonly used models.
Article
Thermodynamics
Meng Zhang, Huan Guo, Ming Sun, Sifeng Liu, Jeffrey Forrest
Summary: The objective and accurate prediction of energy consumption is crucial for the implementation of economic policies and energy development strategy by the government. This paper proposes a novel flexible grey multivariable model that captures the dynamic characteristics of the energy system and is compatible with existing grey models. The model is used to predict the energy consumption of three major provinces in China, and demonstrates superior prediction performance compared to competing models.
Article
Computer Science, Artificial Intelligence
Song Ding, Zui Tao, Jiaqi Hu
Summary: This paper proposes a novel grey multivariable convolution model that addresses the limitations of traditional grey models through collaborative optimization. The Particle Swarm Optimization algorithm is used to determine the optimal parameter values. The empirical results demonstrate the reliability and potential of this novel model for accurate forecasting in high-tech industries.
APPLIED SOFT COMPUTING
(2022)
Article
Thermodynamics
Li Ye, Deling Yang, Yaoguo Dang, Junjie Wang
Summary: Accurately forecasting carbon emissions is of great importance due to the increasing awareness of its impact. However, the dynamic lag relationships between carbon emissions and related factors pose a challenge for prediction. In this study, an enhanced grey forecasting model is proposed, incorporating a time-lag driving term and a linear correction term, to improve the prediction performance by considering the delayed relationships.
Article
Computer Science, Information Systems
Ruijin Wang, Xikai Pei, Juyi Zhu, Zhiyang Zhang, Xin Huang, Jiayi Zhai, Fengli Zhang
Summary: This paper proposes a model fusion-based time series forecasting method to improve the accuracy and efficiency of predictions using multivariate grey model and artificial fish swarm algorithm. Two fusion models based on data decomposition and weighted summation achieve good prediction results in different scenarios.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Xuemei Li, Na Li, Song Ding, Yun Cao, Yao Li
Summary: The rapid growth of solar power generation and its associated fluctuations have significant implications for the security, stability, and productivity of the grid. Accurate predictions of solar power generation are crucial for grid planning and power dispatch. This study proposes a novel data-driven seasonal multivariable grey model that incorporates seasonal factors and time-power items to account for seasonal fluctuations and non-linear trends. Experimental studies comparing the novel model to existing models demonstrate its improved generalizability, stability, and reliability, making it a promising tool for future solar power generation forecasting.
INFORMATION SCIENCES
(2023)
Review
Energy & Fuels
Hui Li, Yunmei Liu, Xilin Luo, Huiming Duan
Summary: Oil consumption forecasting is crucial for economic and social planning due to its impact on the global economy. This paper introduces a new nonlinear multivariable Verhulst model with optimized genetic algorithm, which improves the accuracy of prediction. Experimental results demonstrate that the proposed model outperforms traditional models. The prediction suggests a 24.6641% increase in China's oil consumption by 2024.
Article
Business, Finance
Muhammad Uzair Ali, Zhimin Gong, Muhammad Ubaid Ali, Xiong Wu, Chen Yao
Summary: The study revealed an inverted U-shaped relationship between economic development and carbon dioxide emissions in Pakistan, with fossil energy consumption having a positive impact on carbon emissions and inward foreign direct investment also influencing carbon emissions considerably. The results suggest that Pakistan should focus on promoting sustainable growth through energy efficiency, supporting the use of low GHG emission and efficient technologies, increasing the consumption of renewable energy sources, and directing foreign direct investment towards environment-friendly technologies.
INTERNATIONAL JOURNAL OF FINANCE & ECONOMICS
(2021)
Article
Automation & Control Systems
Weige Nie, Huiming Duan
Summary: In this paper, a novel multivariable grey differential dynamic prediction model is established to analyze and forecast China's carbon emissions. The model considers the influence of the rate of change of the related factors by introducing the differential term of the correlation variables. It also introduces linear correction term and grey action to improve the stability and adaptability of the model. The model shows better simulation and prediction accuracy compared to other models, proving its effectiveness.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Environmental Sciences
Lei Jin, Yuan-hua Chang, Meng Wang, Xin-zhu Zheng, Jian-xun Yang, Jin Gu
Summary: Previous studies have focused more on the relationship between carbon emission reduction, energy consumption, and economic growth in specific countries or regions. This paper explores the co-integration and causality between renewable energy consumption, non-renewable energy consumption, economic growth, and carbon emission using panel data. The results suggest a two-way causal relationship between carbon emissions and economic growth across all economies. There are differences between developed and developing countries in terms of the relationship between economic growth and energy consumption.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2022)
Article
Thermodynamics
Hui Li, Zixuan Wu, Xing Yuan, Yixuan Yang, Xiaoqiang He, Huiming Duan
Summary: The unbalanced development among energy price, energy consumption, and economic growth will destroy the stability of the energy market. By establishing nonlinear differential equations and a grey prediction model group, the coal price and consumption in China can be effectively predicted.
Article
Energy & Fuels
Cheng Zhou, Xiyang Chen
Summary: Accurately predicting energy consumption and carbon emission is crucial for China's carbon peak and carbon neutrality goals. This study proposes a novel three-layer decomposition-ensemble forecasting method that combines the advantages of trend decomposition, empirical mode decomposition, and wavelet decomposition. By breaking down the complex energy consumption prediction task into simpler trend and non-trend subseries prediction tasks, the forecasting performance is improved.
ENERGY STRATEGY REVIEWS
(2023)
Article
Environmental Sciences
Hao Chen, Evelyn Agba Tackie, Isaac Ahakwa, Mohammed Musah, Andrews Salakpi, Morrison Alfred, Samuel Atingabili
Summary: This paper examines the relationship between economic growth, energy consumption, urbanization, population growth, and carbon emissions in the BRICS economies. The findings show that energy consumption worsens environmental quality through high carbon emissions, and economic growth is a significant driver of carbon emissions. However, urbanization and population growth have minimal impact on carbon emissions. There are bidirectional causal connections between economic growth and carbon emissions, energy consumption and economic growth, economic growth and population growth, energy consumption and urbanization, and economic growth and urbanization. Finally, a causal relationship from urbanization to carbon emissions is revealed.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2022)
Article
Green & Sustainable Science & Technology
Changrong Wang, Yun Cao
Summary: This paper introduces two novel grey multivariable models, SRMGM(1, M) and BRMGM(1, m), which optimize the background value and incorporate rolling prediction to enhance the accuracy of forecasting economic growth, energy consumption, and urbanization in China. The results show that the new models outperform the traditional MGM(1, m) model and non-grey models in terms of accuracy, providing a solid basis for government policies and plans regarding economic growth and energy consumption.
JOURNAL OF CLEANER PRODUCTION
(2021)
Article
Environmental Sciences
Hayat Khan, Liu Weili, Itbar Khan, Jianfang Zhang
Summary: This study examines the relationship between natural resources, renewable energy consumption, economic growth, and carbon emissions in 35 Belt and Road Initiative (BRI) countries from 1985 to 2019. Utilizing various regression models, the findings indicate that carbon dioxide and renewable energy are drivers of economic growth, while natural resources impede economic growth. Economic growth and natural resources have a positive impact on carbon dioxide emissions, but renewable energy consumption significantly reduces carbon emissions. The results have important policy implications for BRI countries regarding the influence of natural resources and income inequality on the interplay among renewable energy consumption, economic growth, and carbon emissions.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Green & Sustainable Science & Technology
Hong-Hao Zheng, Zheng-Xin Wang
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2019)
Article
Economics
Zheng-Xin Wang, Ling-Yang He, Dan-Dan Li
Article
Physics, Multidisciplinary
Zheng-Xin Wang, Dan-Dan Li, Hong-Hao Zheng
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2020)
Article
Environmental Sciences
Qin Li, Zheng-Xin Wang, Xiang-Yu Zhang
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2020)
Article
Thermodynamics
Zheng-Xin Wang, Zhi-Wei Wang, Qin Li
Article
Environmental Sciences
Ming-Huan Shou, Zheng-Xin Wang, Dan-Dan Li, Yi Wang
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2020)
Article
Environmental Sciences
Yu-Feng Zhao, Ming-Huan Shou, Zheng-Xin Wang
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2020)
Article
Computer Science, Artificial Intelligence
Zheng-Xin Wang, Yu-Feng Zhao, Ling-Yang He
APPLIED SOFT COMPUTING
(2020)
Article
Mathematics, Applied
Zheng-Xin Wang, Yue-Qi Jv
Summary: A novel grey prediction model based on quantile regression technology (QGM(1,1) model) is proposed to address the issue of traditional grey prediction models being influenced by outliers and lacking stability. The QGM(1,1) model accurately describes the impact of independent variables on the range and shape of dependent variables, as well as captures tail characteristics of the distribution. Results show that the model significantly improves prediction accuracy and enhances robustness.
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
(2021)
Article
Business
Zheng-Xin Wang, Yue-Qi Jv
Summary: The non-linear systematic grey model NSGM(1, m) accurately identifies the non-linear relationship among industrial economic growth, energy consumption, and pollutant emissions, with a good performance in predicting data. The empirical analysis of Zhejiang Province shows detailed predictions for the industrial 3E systems in the region.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2021)
Article
Computer Science, Artificial Intelligence
Zheng-Xin Wang, Ling-Yang He, Yu-Feng Zhao
Summary: A novel gray model based on seasonal dummy variables and its derived model were established to accurately forecast the seasonal fluctuations of US natural gas consumption. Empirical analysis showed that the predicted results outperformed traditional forecasting models, providing insights into the seasonal trends of US natural gas consumption in the coming years.
APPLIED SOFT COMPUTING
(2021)
Article
Business
Ming-Huan Shou, Zheng-Xin Wang, Wen-Qian Lou
Summary: This paper utilizes confirmed cases of COVID-19 from January 20 to March 18, 2020 to construct an SEIR model, evaluating the effects of different non-pharmaceutical interventions. The results show that type-A and type-B interventions can delay the timing of large-scale infections and decrease the peak number of exposed cases, with type-B interventions having more significant effects on susceptible and exposed populations. Type-C interventions focusing on improving recovery rates can effectively reduce the peak number of patients and mortality rate.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2021)
Article
Business, Finance
Zheng-Xin Wang, Yue-Qi Jv
Summary: A smooth difference-in-differences (smooth DID) method is proposed to examine the effects of banking deregulation on income distribution in the United States. The findings confirm that bank deregulation gradually reduces income inequality.
FINANCE RESEARCH LETTERS
(2022)
Article
Economics
Zheng-Xin Wang, Wen-Qian Lou, Ling-Ling Pei
TECHNOLOGICAL AND ECONOMIC DEVELOPMENT OF ECONOMY
(2020)
Article
Mathematics, Interdisciplinary Applications
Zheng-Xin Wang, Ji-Min Wu, Chao-Jun Zhou, Qin Li
GREY SYSTEMS-THEORY AND APPLICATION
(2020)
Article
Green & Sustainable Science & Technology
Lars odegaard Bentsen, Narada Dilp Warakagoda, Roy Stenbro, Paal Engelstad
Summary: This study investigates uncertainty modeling in wind power forecasting using different parametric and non-parametric methods. Johnson's SU distribution is found to outperform Gaussian distributions in predicting wind power. This research contributes to the literature by introducing Johnson's SU distribution as a candidate for probabilistic wind forecasting.
JOURNAL OF CLEANER PRODUCTION
(2024)
Article
Green & Sustainable Science & Technology
Xing Liu, Qiuchen Wang, Yunhao Wen, Long Li, Xinfang Zhang, Yi Wang
Summary: This study analyzes the characteristics of process parameters in three lean gas ethane recovery processes and establishes a prediction and multiobjective optimization model for ethane recovery and system energy consumption. A new method for comparing ethane recovery processes for lean gas is proposed, and the addition of extra coolers improves the ethane recovery. The support vector regression model based on grey wolf optimization demonstrates the highest prediction accuracy, and the multiobjective multiverse optimization algorithm shows the best optimization performance and diversity in the solutions.
JOURNAL OF CLEANER PRODUCTION
(2024)
Article
Green & Sustainable Science & Technology
Cairong Song, Haidong Yang, Xian-Bing Meng, Pan Yang, Jianyang Cai, Hao Bao, Kangkang Xu
Summary: The paper proposes a novel deep learning-based prediction framework, aTCN-LSTM, for accurate cooling load predictions. The framework utilizes a gate-controlled multi-head temporal convolutional network and a sparse probabilistic self-attention mechanism with a bidirectional long short-term memory network to capture both temporal and long-term dependencies in the cooling load sequences. Experimental results demonstrate the effectiveness and superiority of the proposed method, which can serve as an effective guide for HVAC chiller scheduling and demand management initiatives.
JOURNAL OF CLEANER PRODUCTION
(2024)
Article
Green & Sustainable Science & Technology
Zhe Chen, Xiaojing Li, Xianli Xia, Jizhou Zhang
Summary: This study uses survey data from the Loess Plateau in China to evaluate the impact of social interaction on the adoption of soil and water conservation (SWC) technology by farmers. The study finds that social interaction increases the likelihood of farmers adopting SWC, and internet use moderates this effect. The positive impact of social interaction on SWC adoption is more pronounced for farmers in larger villages and those who join cooperative societies.
JOURNAL OF CLEANER PRODUCTION
(2024)
Article
Green & Sustainable Science & Technology
Chenghua Zhang, Yunfei Yan, Kaiming Shen, Zongguo Xue, Jingxiang You, Yonghong Wu, Ziqiang He
Summary: This paper reports a novel method that significantly improves combustion performance, including heat transfer enhancement under steady-state conditions and adaptive stable flame regulation under velocity sudden increase.
JOURNAL OF CLEANER PRODUCTION
(2024)